Systematic review and open challenges in hyper-heuristics usage on expensive optimization problems with limited number of evaluations

Ever since the early introduction of optimization by Kantorovich in 1939 the science and engineering researchers have created vast categories of optimization problems. Throughout the years, these optimization problems moved from classical problems to more challenging complex problems and these trans...

Full description

Saved in:
Bibliographic Details
Main Authors: Jia, Hui Ong, Teo, Jason Tze
Format: Proceedings
Language:en
en
Published: IEEE 2021
Subjects:
Online Access:https://eprints.ums.edu.my/id/eprint/32494/1/Systematic%20review%20and%20open%20challenges%20in%20hyper-heuristics%20usage%20on%20expensive%20optimization%20problems%20with%20limited%20number%20of%20evaluations.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/32494/2/Systematic%20review%20and%20open%20challenges%20in%20hyper-heuristics%20usage%20on%20expensive%20optimization%20problems%20with%20limited%20number%20of%20evaluations.pdf
https://eprints.ums.edu.my/id/eprint/32494/
https://ieeexplore.ieee.org/document/9509993
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Ever since the early introduction of optimization by Kantorovich in 1939 the science and engineering researchers have created vast categories of optimization problems. Throughout the years, these optimization problems moved from classical problems to more challenging complex problems and these transformations were direct results of industrials demands. Consequently, this has given rise to one of the new classes of challenging optimization problems known as expensive optimization. A problem is considered expensive when it involves very high computational costs due to the complex evaluations of highdimensional and time-consuming objective functions. In this paper, the past researches that were done in this new research domain are presented followed by a discussion of the hyper-heuristics history and how hyper-heuristics is used in solving expensive optimization problems especially in expensive optimization with a limited number of evaluations.